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mcp-use is a unified MCP framework to build MCP servers, MCP clients and MCP Agents.
SectionDescription
AgentAn AI Agent with tool calling capabilities through MCP connectionsExamples
ClientA full featured MCP Client implementation for Python with async supportExamples
ServerThe MCP Server framework implementation for PythonExamples

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MCP Agent

mcp-use has a complete MCP Agent implementation for Python. MCP Agents are AI-powered agents that can use tools from MCP servers to accomplish complex tasks. They reason across multiple steps, selecting and executing tools as needed. Building such agents is easy with mcp-use, all you need is an LLM and the MCP Client. 
pip install mcp-use
Here’s a simple example to get you started for an agent with browser tools support. 
import asyncio
from langchain_openai import ChatOpenAI  # use your preferred LLM provider
from mcp_use import MCPAgent, MCPClient

async def main():
    # Create MCPClient from configuration object
    client = MCPClient({
        "mcpServers": {
            "playwright": {
                "command": "npx",
                "args": ["@playwright/mcp@latest"],
                "env": {
                    "DISPLAY": ":1"
                }
            }
        }
    })

    # Create agent with the client
    agent = MCPAgent(
        llm=ChatOpenAI(model="gpt-4o"),  # use your preferred LLM provider
        client=client,
        max_steps=30
    )

    # Run the query
    result = await agent.run(
        "Find the best restaurant in San Francisco USING GOOGLE SEARCH"
    )
    print(f"\nResult: {result}")

    # Clean up
    await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(main())
For multi-server setups, tool restrictions, and advanced configuration options, see the Agent Configuration and Client Configuration guides.
For a complete overview of the mcp-use MCP Agent, see the MCP Agent documentation.

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Available MCP Servers

mcp-use supports any MCP server. Check out the Awesome MCP Servers list for available options. Or deploy your own following the instruction below.

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MCP Client

mcp-use has a complete MCP Client implementation for Python with full async support. It supports stdio and HTTP with SSE transports for connecting to any MCP server. 
pip install mcp-use
Then create a new MCP Client: 
import asyncio
from mcp_use import MCPClient

async def main():
    client = MCPClient({
        "mcpServers": {
            "everything": {
                "command": "npx",
                "args": ["-y", "@modelcontextprotocol/server-everything"]
            }
        }
    })

    # Initialize all configured sessions
    await client.create_all_sessions()

    # Get the session for a specific server
    session = client.get_session("everything")

    # List available tools
    tools = await session.list_tools()
    print(f"Available tools: {[t.name for t in tools]}")

    # Call a specific tool with arguments
    result = await session.call_tool("add", {"a": 1, "b": 2})
    print(f"Result: {result}")

    # Clean up
    await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(main())
To learn more about the MCP Client, see the MCP Client documentation.

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MCP Server

mcp-use has a complete MCP server framework implementation for Python. It supports all official MCP features including tools, resources, prompts, and middleware. 
pip install mcp-use
Create a simple MCP server: 
from mcp_use import MCPServer

server = MCPServer(
    name="my-server",
    version="1.0.0",
    description="My custom MCP server"
)

# Define a tool
@server.tool()
def get_weather(city: str) -> dict:
    """Get weather for a city"""
    return {"temperature": 72, "condition": "sunny", "city": city}

# Start the server
if __name__ == "__main__":
    server.run()
To learn more about the MCP Server, see the MCP Server documentation.

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Next Steps

Agent Configuration

Configure your Agent and connected MCP servers

LLM Integration

Explore supported LLM providers and their setup

Examples

Browse real-world examples and use cases
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